In the computer vision field, visual causal discovery has been spearheaded by tasks related to the CLEVRER dataset [1]} where ML algorithms are asked to understand a video scene and answer counterfactual questions.[2]} learn to predict causal links from videos, by parameterizing the causal links as strings and springs where the algorithm predicts their parameters. [3]} perform a similar task by inferring stationary causal graphs from videos. Furthermore, [4]} develop a causal discovery method based upon the use of attention-enabled convolutional neural networks. [5]} address the task of causal discovery by training a neural network to induce causal dependencies by predicting causal adjacency matrices between variables. Finally, works like [6]} analyze how humans perform the task of causal discovery in relation to how machine learning approaches the same task.
